Relational database management systems have been widely used for the past several decades to store information. Information stored may include enterprise data, such as manufacturing information, logistical data, financial records and personnel data. Typical systems include IBM DB2™, ORACLE™, MySQL™, MICROSOFT SQLServer™, PostgreSQL™, MICROSOFT ACCESS™ and SQLite™. With the advent of the “Age of Big Data”, these systems continue to be invaluable as enterprise tools. However, as datasets continue to grow exponentially, conventional procedures for implementing these and other systems have become prohibitively clumsy, time-consuming and error-prone. Updating databases and their constituent subsystems, for example, carry an ever-increasing risk of intentional and unintentional corruption of database entries.
During conventional parameter updating procedures, even when performed by a database programmer, unintentional “fat-fingering” and/or incorrect insertions of updates and changes may result in corruption of the datasets. Undetected, these errors can be devastating for enterprises and individuals who rely on the accuracy of the data.
According to conventional methods of database management, there is limited ability to detect, trace or track these types of errors. Furthermore, with the rising threat of malware and other cyberattacks on business and other enterprises, there is a growing need to secure data-updating processes.
It would be desirable, therefore, to provide methods and apparatus for streamlining updating of parameters in mainframe relational database management software. It would also be desirable to provide methods and apparatus for reducing errors in the updating of the parameters. It would further be desirable to provide methods and apparatus for reducing risk of malicious corruption of data stored in an enterprise database. It would also be desirable to provide methods and apparatus for detecting and/or tracking database parameter updates and/or errors.